Introduction

Australia is fast becoming a hotspot for AI-generated cybercrime. Risks that were once theoretical are now costing businesses millions, even as security teams deploy advanced analytics, AI, and new approaches to keep pace.

Consider this case in 2024. A finance employee in Hong Kong wired US$25 million during what appeared to be a routine video call with the CFO and other colleagues. In reality, every other participant was an AI-generated deepfake, convincing enough to override the employee’s initial suspicion and only discovered after the funds had been transferred across 15 transactions.

This isn’t a distant problem. In fact, the same techniques are already surfacing on our shores.  Mastercard research found that 20% of Australian businesses have received deepfake threats in the past 12 months, and of these, 12% fell for the manipulated content.

Ultimately, the issue isn’t a lack of security tools, but the speed at which they operate. In today’s era of AI-driven fraud, defences must move at real-time speed, and that requires a shift in data architecture.

Fraud Moves Fast, So Your Data Architecture Needs to Move Faster

AI-powered fraud rarely happens as a single event. Attackers find a weak spot, and with an opening, they build momentum. A convincing voice message might be followed by a login attempt, then a fake video call or urgent payment request. Each touchpoint looks ordinary on its own, but together they form a coordinated attack chain.

This is why data streaming technology has a critical role to play in building a modern fraud defence.

When data can be continuously processed and analysed, anti-fraud systems are able to detect suspicious behaviour as it unfolds. Real-time platforms preserve the sequence, timing, and context of each action, enabling them to correlate signals across channels and quickly highlight if customers are about to come under attack.

The challenge is, however, that many businesses continue to work with batch processing or siloed tools, which take hours to analyse activity in chunks, at a later time. By the time anomalies are flagged, the funds have often moved offshore and the opportunity to intervene has passed.

According to Confluent’s 2025 Data Streaming Report, 41% of Australian IT leaders cite out-of-date data as a frequent challenge, while 46% cite fragmented data ownership. Until these gaps are addressed, fraudsters will continue to exploit the lag between when threats emerge and when organisations are able to act.

Trust Depends On Speed, Not Delays

Trust is another reason why speed matters. Each time a fraudulent attack slips through the cracks, organisations often respond by introducing new layers of friction: extra verification steps, delayed transfers, or blocked accounts. And while these steps can be valuable for safeguarding customers, they lead to frustration and detract from the customer experience when overused or ill-timed.

Real-time data streaming offers another solution. Instead of treating every interaction the same way, businesses can shift to contextual, risk-based decision-making and adjust how they respond based on detected risks. Suspicious behaviour can trigger tighter checks on the spot, while normal activity can proceed without interruption. Customers may not see these defences operating in the background, but they experience the benefits through faster, smoother interactions.

For Australian organisations, particularly in finance, where even a few moments of delay can have a serious monetary impact, getting this right is essential. People expect their money to move quickly and securely, without unnecessary hurdles. By building fraud defences on a foundation of real-time data architecture,  businesses can deliver seamless protection without impacting customer ease and trust.

Build the Foundations of a Real-time Fraud Defence

But what does a modern defence look like in practice? Ultimately, it comes down to three core capabilities. The first is the ability to detect and analyse every signal, not just transactions.  Signals of suspicious activity often hide in plain sight: in logins, device fingerprints, payment speeds and user behaviour. It’s only when these inputs are streamed together in a single pipeline that patterns emerge—patterns that batch systems may miss entirely.

It’s just as important for systems to be able to learn and adapt continuously based on fresh intelligence. Even the most sophisticated fraud models fall behind if they rely on stale or delayed data. Streaming enables models to adapt in real-time, creating a tight feedback loop and reducing false positives.

And lastly, systems need to be able to respond instantly with automated actions. With a robust real-time architecture, a suspicious signal can trigger a payment to be paused, a customer to be alerted, or escalations to be made internally in moments.

Together, these three elements shift an organisation’s fraud defence from being reactive to proactive, equipping businesses with the speed and agility needed to meet today’s evolving threats.

Strengthen Resilience Through Real-time Architecture

Fraud tactics are always evolving. Just as security teams learn one approach, criminals pivot to another tactic and vulnerability. But the speed and sophistication of AI-generated fraud has exposed the limitations of traditional fraud detection, and its lags, silos, and lack of contextual awareness are putting businesses and customers at risk.

The answer isn’t more dashboards or stricter rules. It’s a shift in foundation to an architecture that’s built to move at the same speed and complexity as today’s threats. And when information flows instantly and coherently across systems, enabling real-time pattern recognition and instant, automated actions, organisations can act before losses occur, and protect customers without adding friction.